Quantification of local circulation with oct angiography

ABSTRACT

Impaired intraocular blood flow within vascular beds in the human eye is associated with certain ocular diseases including, for example, glaucoma, diabetic retinopathy and age-related macular degeneration. A reliable method to quantify blood flow in the various intraocular vascular beds could provide insight into the vascular component of ocular disease pathophysiology. Using ultrahigh-speed optical coherence tomography (OCT), a new 3D angiography algorithm called split-spectrum amplitude-decorrelation angiography (SSADA) was developed for imaging microcirculation within different intraocular regions. A method to quantify SSADA results was developed and used to detect perfusion changes in early stage ocular disease. Associated embodiments relating to methods for quantitatively measuring blood flow at various intraocular vasculature sites, systems for practicing such methods, and use of such methods and systems for diagnosing certain ocular diseases are herein described.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims benefit of U.S. Provisional PatentApplication No. 61/699,257 filed Sep. 10, 2012, entitled “QUANTIFICATIONOF LOCAL CIRCULATION WITH OCT ANGIOGRAPHY”, and to U.S. ProvisionalPatent Application No. 61/799,502 filed Mar. 15, 2013, entitled “IN VIVOQUANTITATIVE OPTICAL FLOW IMAGING”. Priority to these provisionalapplications is expressly claimed, and the disclosures of theseprovisional applications are herein incorporated by reference in theirentirety.

ACKNOWLEDGMENT OF GOVERNMENT SUPPORT

This invention was made with government support under grant numbersR01-EY013516 awarded by the National Institutes of Health. Thegovernment has certain rights in the technology.

TECHNICAL FIELD

This disclosure relates generally to the field of biomedical imaging,and more specifically to methods, apparatuses, and systems associatedwith optical coherence tomography and angiography.

BACKGROUND

Diabetic retinopathy, age-related macular degeneration and glaucoma arethe typical ocular diseases in which vascular disorders and impairedcirculation have been observed. For example, an increasing body ofevidence suggests that dysfunction of ocular microcirculation in theoptic nerve influences the progression of glaucoma. Quantification ofocular circulation is therefore important for the diagnosis ofophthalmic diseases.

Currently, a number of methods have been used for measuring ocularperfusion. Fluorescein angiography (FA) provides useful qualitativeinformation in health and disease; however, it only shows thesuperficial retinal vessels and does not assess deep perfusion, such asthe microcirculation in optic nerve head (ONH), choroidal blood flow.Additionally, injection of dye can cause nausea and anaphylaxis, makingit unsuitable as a tool for routine glaucoma assessment. Both laserDoppler flowmetry [e.g. Heidelberg Retinal Flowmeter (HRF)], whichsamples capillary flow over a small retinal area, and laser speckleflowgraphy, which provides a spot sample of blood velocity, can showdifferences between diseases and normal groups. However the measurementsprovided by these methods are too variable for diagnostic applicationdue to dependence on the signal strength and the location of the smallsampled area. Magnetic resonance imaging (MRI) has been proposed toquantitatively image ONH perfusion; however, the major limiting factorwith this method is the small size of the ONH and limited resolution todetect focal or mild circulatory insufficiency.

Optical coherence tomography (OCT) is an imaging technique that has beenwidely used for diagnosis and management of ocular diseases. As acoherence detection technique, OCT can detect the Doppler frequencyshift of the backscattered light that provides information on bloodflow. Doppler OCT has been used for measuring total human retinal bloodflow (TRBF) in patients with glaucoma, optic neuropathy, and diabeticretinopathy. With this method, global blood flow from central retinalvessels can be quantified, but local microcirculation cannot be resolvedbecause the velocity range is too low for accurate Doppler measurement.To measure local microcirculation, we recently developed thesplit-spectrum amplitude-decorrelation angiography (SSADA) algorithmthat provides high quality three-dimensional (3D) angiography usingultrahigh speed OCT. Because SSADA is based on the variation ofreflectance in resolution cells of isotropic dimensions, it is equallysensitive to transverse and axial movements. Thus it may be able toprovide more impartial estimates of local microvascular perfusion thatare independent of beam incidence angle. In contrast, both Doppler andphase-variance OCT angiography are more sensitive to axial flow than totransverse flow. Therefore SSADA may be a good basis for quantitativeangiography of the microcirculation within different vascular beds.

In vivo three-dimensional mapping of biologic tissue and vasculature isdifficult due to the highly-scattering and absorptive nature of biologictissue. Some current methods have slow scanning speeds making in vivothree-dimensional imaging difficult. Some other techniques having fasterscanning speeds are still lacking due to their inability to scan deeplyinto biologic tissue without producing overlapped images, requiring theuse of invasive procedures to scan the tissue of interest. Manytechniques aimed at deeper imaging generally cannot provide deep imagingof tissue having moving material (e.g., blood flow). Therefore, methodsto effectively image structure and/or tissue movement, such as bloodflow, are of substantial clinical importance.

Optical coherence tomography (OCT) is an imaging modality forhigh-resolution, depth-resolved cross-sectional, and 3-dimensional (3D)imaging of biological tissue. Among its many applications, ocularimaging in particular has found widespread clinical use. In the lastdecade, due to the development of light source and detection techniques,Fourier-domain OCT, including spectral (spectrometer-based) OCT andswept-source OCT, have demonstrated superior performance in terms ofsensitivity and imaging speed over those of time-domain OCT systems. Thehigh-speed of Fourier-domain OCT has made it easier to image not onlystructure, but also blood flow. This functional extension was firstdemonstrated by Doppler OCT which images blood flow by evaluating phasedifferences between adjacent A-line scans. Although Doppler OCT is ableto image and measure blood flow in larger blood vessels, it hasdifficulty distinguishing the slow flow in small blood vessels frombiological motion in extravascular tissue. In the imaging of retinalblood vessels, Doppler OCT faces the additional constraint that mostvessels are nearly perpendicular to the OCT beam, and therefore thedetectability of the Doppler shift signal depends critically on the beamincident angle. Thus, other techniques that do not depend on beamincidence angle are particularly attractive for retinal and choroidalangiography.

Several OCT-based techniques have been successfully developed to imagemicrovascular networks in human eyes in vivo. One example is opticalmicroangiography (OMAG), which can resolve the fine vasculature in bothretinal and choroid layers. OMAG works by using a modified Hilberttransform to separate the scattering signals from static and movingscatters. By applying the OMAG algorithm along the slow scanning axis,high sensitivity imaging of capillary flow can be achieved. However, thehigh-sensitivity of OMAG requires precise removal of bulk-motion byresolving the Doppler phase shift. Thus, it is susceptible to artifactsfrom system or biological phase instability. Other related methods suchas phase variance and Doppler variance have been developed to detectsmall phase variations from microvascular flow. These methods do notrequire non-perpendicular beam incidence and can detect both transverseand axial flow. They have also been successful in visualizing retinaland choroidal microvascular networks. However, these phase-based methodsalso require very precise removal of background Doppler phase shifts dueto the axial movement of bulk tissue. Artifacts can also be introducedby phase noise in the OCT system and transverse tissue motion, and thesealso need to be removed.

To date, most of the aforementioned approaches have been based onspectral OCT, which provides high phase stability to evaluate phaseshifts or differentiates the phase contrast resulting from blood flow.Compared with spectral OCT, swept-source OCT introduces another sourceof phase variation from the cycle-to-cycle tuning and timingvariabilities. This makes phase-based angiography noisier. To usephase-based angiography methods on swept-source OCT, more complexapproaches to reduce system phase noise are required. On the other hand,swept-source OCT offers several advantages over spectral OCT, such aslonger imaging range, less depth-dependent signal roll-off, and lessmotion-induced signal loss due to fringe washout. Thus an angiographymethod that does not depend on phase stability may be the best choice tofully exploit the advantages of swept-source OCT. In this context,amplitude-based OCT signal analysis may be advantageous for ophthalmicmicrovascular imaging.

One difficulty associated with OCT's application in microvascularimaging comes from the prevalent existence of speckle in OCT imagesobtained from in vivo or in situ biological samples. Speckle is theresult of the coherent summation of light waves with random path lengthsand it is often considered as a noise source which degrades the qualityof OCT images. Various methods have been developed to reduce speckle inspatial domain, such as angle compounding, spectral compounding, andstrain compounding. Speckle adds to “salt-and-pepper-like” noise to OCTimages and induces random modulation to interferometric spectra whichcan significantly reduce contrast.

In spite of being a noise source, speckle also carries information.Speckle patterns form due to the coherent superposition of randomphasors. As a result of speckle, the OCT signal becomes random in anarea that is macroscopically uniform. If a sample under imaging isstatic, the speckle pattern is temporally stationary. However, whenphotons are backscattered by moving particles, such as red blood cellsin flowing blood, the formed speckle pattern will change rapidly overtime. Speckle decorrelation has long been used in ultrasound imaging andin laser speckle technique to detect optical scattering from movingparticles such as red blood cells. This phenomenon is also clearlyexhibited by real-time OCT reflectance images. The scattering pattern ofblood flow varies rapidly over time. This is caused by the fact that theflow stream drives randomly distributed blood cells through the imagingvolume (voxel), resulting in decorrelation of the received backscatteredsignals that are a function of scatterer displacement over time. Thecontrast between the decorrelation of blood flow and static tissue maybe used to extract flow signals for angiography.

The speckle phenomenon has been used in speckle variance OCT for thevisualization of microvasculature. Speckle patterns at areas withflowing blood have a large temporal variation, which can be quantifiedby inter-frame speckle variance. This technique termed “specklevariance” has been used with swept-source OCT demonstrating asignificant improvement in capillary detection in tumors by calculationof the variance of the OCT signal intensity. A key advantage of thespeckle variance method is that it does not suffer from phase noiseartifacts and does not require complex phase correction methods.Correlation mapping is another amplitude-based method that has alsorecently demonstrated swept-source OCT mapping of animal cerebral andhuman cutaneous microcirculation in vivo. These amplitude-basedangiography methods are well suited to swept-source OCT and offervaluable alternatives to the phase-based methods. However, such methodsstill suffer from bulk-motion noise in the axial dimension where OCTresolution is very high. Therefore, an amplitude-based swept-sourceangiography method that is able to reduce bulk-motion noise withoutsignificant sacrifice in the flow signal would be optimal. For example,imaging of retinal and choroidal flow could be particularly improvedwith such noise reduction, as in the ocular fundus the flow signal ispredominantly in the transverse rather than axial dimension.

While improving qualitative blood flow measurement through noisereduction methods has immense value, determining quantitative blood flowmeasurement in regions of interest is highly desirable clinically. Todate, while several methods exist to measure global retinal blood flow,they have significant limitations and are not used clinically. Forexample, ultrasound color Doppler imaging does not have sufficientspatial resolution to measure retinal vessels. It measures velocity andresistive indices in large retrobulbar vessels. Although studies haveshown differences between normal and glaucoma groups using ultrasoundcolor Doppler imaging, variability in measurements has limited itspotential for clinical diagnosis. Bidirectional laser Dopplervelocimetry can measure velocity and flow in individual retinal vessels,but measurement of total retinal blood flow is too time consuming to bepractical. Dicon's pulsatile ocular blood flow analyzer can analyzeintraocular pressure, but has been shown to be a poor correlate ofocular circulation. Finally, dual-angle Doppler OCT has the limitationof requiring special hardware that is not compatible with existingcommercial OCT designs.

As noted above, Doppler OCT, on its own, has difficulty distinguishingthe slow flow in small blood vessels from biological motion inextravascular tissue, as well as has difficulty detecting and definingvessel anatomy due to most vessels being nearly perpendicular to the OCTbeam. However, Doppler shift can provide valuable quantitative velocityinformation. Thus, an optimal bulk-noise reduction amplitude-basedswept-source angiography method used in combination with Doppler OCT(e.g., via dual scanning, done simultaneously or near simultaneously)could allow for measurement of total retinal blood flow (TRBF),including both vein and artery measurement around the optic disc.

SUMMARY

As described below, a new quantitative system has been established formaking quantitative ocular blood flow measurements. Specifically, themethods described herein are able to measure circulation in ONH (e.g.whole disc/temporal ellipse, peripapillary retina, peripapillarychoroid) and macula (e.g. macular retina, macular choroid, foveaavascular zone and the area of non-perfusion). Quantification ofdifferent circulation obtained from patients having an ocular diseasecompared with the result from normal subjects are herein described.

One embodiment is directed to methods for quantitatively measuring bloodflow in an ocular vascular bed. In certain embodiments, the method forquantitatively measuring blood flow in an ocular vascular bed maycomprise the steps of selecting an ocular vascular bed from which toquantitatively measure blood flow, scanning the ocular vascular bed toobtain M-B scans of OCT spectrum therefrom, splitting the M-B scans ofOCT spectrum into M spectral bands and determining a quantitativemeasurement of blood flow from the M spectral bands. In additionalembodiments, the step of splitting the M-B scans of the OCT spectruminto M spectral bands may comprise creating overlapping filters coveringthe OCT spectrum, and filtering the OCT spectrum with the overlappingfilters. In yet further embodiments, the step of determining aquantitative measurement of blood flow from the M spectral bands maycomprise creating decorrelation images for the M spectral bands andcombining the decorrelation images for the M spectral bands to create aflow image and/or the step of creating decorrelation images for the Mspectral bands may comprise determining amplitude information for eachspectral band and calculating decorrelation between adjacent amplitudeframes for each spectral band.

Yet other embodiments are directed to methods for quantitativelymeasuring blood flow in an ocular vascular bed as described above thatfurther comprise the step of removing background noise. The methodsdescribed herein may optionally also comprise combining thedecorrelation images for the M spectral bands to create a flow imagethat comprises averaging the decorrelation images for each spectral bandto create an average decorrelation image for each spectral band andaveraging the averaged decorrelation images from the M spectral bands.The ocular vascular bed from which quantitative blood flow measurementsare obtained may include the ocular nerve head, the macula, the oculardisc, the temporal ellipse, the peripapillary retina, the peripapillarychoroid, the macular retina, the macular choroid, the fovea avascularzone, or the area of non-perfusion.

Other embodiments are directed to use of the herein described methodsfor quantitatively measuring blood flow in an ocular vascular bed forthe purpose of diagnosing an ocular disease. In this regard, it is notedthat ocular diseases such as glaucoma, diabetic retinopathy andage-related macular degeneration are associated with impairedintraocular blood flow and circulation.

Yet further embodiments are directed to specific systems useful forquantitatively measuring blood flow in an intraocular vascular bed. Incertain embodiments, such systems may comprise an optical coherencetomography apparatus and one or more processors coupled to the OCTapparatus and adapted to cause that apparatus to obtain M-B scans of OCTspectrum from the ocular vascular bed, split the M-B scans of OCTspectrum into M spectral bands, and determine a quantitative measurementof blood flow from the M spectral bands.

Additional embodiments will be readily apparent to one skilled in theart upon reading the present specification.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments described herein will be readily understood by the followingdetailed description in conjunction with the accompanying drawings.Embodiments are illustrated by way of example and not by way oflimitation in the figures of the accompanying drawings.

FIG. 1 is a chart comparing prior art techniques with those describedherein with regard to vascular connectivity and decorrelationsignal/noise (DSNR).

FIG. 2 schematically illustrates modification of an OCT imagingresolution cell to create an isotropic resolution cell utilizing aband-pass filter.

FIG. 3 schematically illustrates M-B-scan mode for acquiring OCTspectrum.

FIG. 4 is a flowchart showing an exemplary method for creating adecorrelation (flow) image that uses split-spectrum techniques and thefull information in the entire OCT spectral range.

FIG. 5 is a flowchart showing additional exemplary methods of theexemplary method of FIG. 4.

FIG. 6 schematically illustrates a 2D spectral interferogram split intodifferent frequency bands.

FIG. 7 schematically illustrates the methods of FIG. 4 and FIG. 5 forcreating a decorrelation (flow) image that uses split-spectrumtechniques and the full information in the entire OCT spectral range.

FIG. 8 is a flowchart showing an exemplary method for eliminatingdecorrelation images with excessive motion noise.

FIG. 9 schematically illustrates an in vivo imaging system forcollecting image information.

FIG. 10 illustrates an embodiment of an in vivo imaging system inaccordance with various embodiments described herein.

FIG. 11 illustrates an embodiment of an article of manufacture for invivo imaging in accordance with various embodiments described herein.

FIG. 12 illustrates in vivo 3-D volumetric structure images of the opticnerve head using imaging methods in accordance with various embodimentsdescribed herein.

FIG. 13 illustrates in vivo 3-D volumetric structure images of themacula using methods in accordance with various embodiments describedherein.

FIG. 14 illustrates in vivo images of macular retinal circulation usingmethods in accordance with prior art methods and in accordance withvarious embodiments described herein.

FIG. 15 illustrates in vivo images depicting vascular connectivity andsignal to noise ratio (SNR) using methods in accordance with prior artmethods and in accordance with various embodiments described herein.

FIG. 16 illustrates in vivo images using methods in accordance withvarious embodiments described herein, including multiple rings circularscan pattern and Doppler angle estimation for a single vessel.

FIGS. 17A-D illustrates in vivo images using methods in accordance withvarious embodiments described herein, including multiple rings circularscan Doppler OCT in combination with SSADA to provide total retinalblood flow (TRBF) measurement.

FIG. 18 schematically illustrates an OCT angiography scan pattern forblood flow quantification.

FIG. 19 schematically shows the creation of an isotropic resolutioncell.

FIG. 20 is a flowchart showing an exemplary method for conductingquantitative blood flow measurements.

FIG. 21 illustrates the specific segmentation of various ocular vascularbeds.

FIG. 22 illustrates an exemplary alternative segmentation method for theocular nerve head (OHN).

FIG. 23 illustrates an exemplary method for detecting disc margin.

FIG. 24 illustrates an exemplary method for quantifying ONH blood flow.

FIG. 25 illustrates detection of the fovea avascular zone and otherareas of non-perfusion.

FIG. 26 illustrates OCT angiography showing reduced ONH blood flow inglaucoma.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof. It is to be understoodthat other embodiments may be utilized and structural or logical changesmay be made.

Various operations may be described as multiple discrete operations inturn, in a manner that may be helpful in understanding embodimentsdescribed herein; however, the order of description should not beconstrued to imply that these operations are order dependent.

The description may use perspective-based descriptions such as up/down,back/front, and top/bottom. Such descriptions are merely used tofacilitate the discussion and are not intended to restrict theapplication of embodiments described herein.

The description may use the phrases “in an embodiment,” or “inembodiments,” which may each refer to one or more of the same ordifferent embodiments. Furthermore, the terms “comprising,” “including,”“having,” and the like, as used with respect to embodiments describedherein, are synonymous.

A phrase in the form of “A/B” means “A or B.” A phrase in the form “Aand/or B” means “(A), (B), or (A and B).” A phrase in the form “at leastone of A, B and C” means “(A), (B), (C), (A and B), (A and C), (B and C)or (A, B and C).” A phrase in the form “(A) B” means “(B) or (A B),”that is, A is optional.

In various embodiments described herein, methods, apparatuses, andsystems for biomedical imaging are provided. In exemplary embodimentsdescribed herein, a computing system may be endowed with one or morecomponents of the disclosed articles of manufacture and/or systems andmay be employed to perform one or more methods as disclosed herein.

In various embodiments, structure and/or flow information of a samplemay be obtained using optical coherence tomography (OCT) (structure) andOCT angiography (structure and flow) imaging based on the detection ofspectral interference. Such imaging may be two-dimensional (2-D) orthree-dimensional (3-D), depending on the application. Structuralimaging may be of an extended depth range relative to prior art methods,and flow imaging may be performed in real time. One or both ofstructural imaging and flow imaging as disclosed herein may be enlistedfor producing 2-D or 3-D images.

Unless otherwise noted or explained, all technical and scientific termsused herein are used according to conventional usage and have the samemeaning as commonly understood by one of ordinary skill in the art whichthe disclosure belongs. Although methods, systems, andapparatuses/materials similar or equivalent to those described hereincan be used in the practice or testing of the present disclosure,suitable methods, systems, and apparatuses/materials are describedbelow.

All publications, patent applications, patents, and other referencesmentioned herein are incorporated by reference in their entirety. Incase of conflict, the present specification, including explanation ofterms, will control. In addition, the methods, systems, apparatuses,materials, and examples are illustrative only and not intended to belimiting.

In order to facilitate review of the various embodiments of thedisclosure, the following explanation of specific terms is provided:

A-scan: A reflectivity profile that contains information about spatialdimensions and location of structures with an item of interest (e.g., anaxial depth scan).

Autocorrelation: A cross-correlation of a signal with itself; thesimilarity between observations as a function of the time separationbetween them. For example, autocorrelation can be used to find repeatingpatterns, such as the presence of a periodic signal which has beenburied under noise, or used to identify the missing fundamentalfrequency in a signal implied by its harmonic frequencies.

B-scan: A cross-sectional tomograph that may be achieved by laterallycombining a series of axial depth scans (e.g., A-scans).

Cross-correlation: A measure of similarity of two waveforms as afunction of a time-lag applied to one of the waveforms.

Decorrelation: A process that is used to reduce autocorrelation within asignal, or cross-correlation within a set of signals, while preservingother aspects of the signal. For example, decorrelation can be used toenhance differences found in each pixel of an image. A measure of a lackof correlation or similarity between corresponding pixels in two imagescan also describe decorrelation. The end result of a decorrelationprocess is that faint information within a signal may be enhanced tobring out (e.g., present) subtle differences that may be meaningful. Forexample, one can calculate decorrelation to find a difference betweenimages.

Illustrated in FIG. 1 is a comparison chart 100 of prior artamplitude-based OCT signal analysis methods and the embodimentsdescribed herein based on vascular connectivity and decorrelationsignal/noise (DSNR). Full-spectrum decorrelation method 100, forexample, can be utilized as the baseline value for comparison purposes,however, as described previously, it is sensitive to axial bulk motioncausing significant noise in the resulting images produced. In pixelaveraging method 112 the signal in several adjacent pixels is combinedresulting in an improvement of decorrelation signal-to-noise ratio(DSNR). The improved DSNR of pixel averaging method 112 in turn leads tohigher quality images of microcirculation (compared to full-spectrumdecorrelation method 100), which can be assessed by measuring thevasculature of the microvascular network revealed in the OCT angiograms.As described herein, split-spectrum decorrelation 122 further improvesDSNR (compared to the improvement offered by pixel averaging method 112)by reducing the noise due to axial bulk motion. This can be accomplishedby the methods described herein below (e.g., reducing the axialdimension of the effective resolution cell).

The improved DSNR of split-spectrum decorrelation method 122 in turnleads to even higher quality images of microcirculation (compared tofull-spectrum decorrelation method 100 and pixel averaging method 112),which can be assessed by measuring the vasculature of the microvascularnetwork revealed in the OCT angiograms. Such an improvement, can allowfor images and information useful in for diagnostic and management ofdiseases in the eye, as well as investigations and analysis ofcirculation, angiogenesis and the other blood flow imaging analysis.Additionally, the split-spectrum decorrelation 122 could be used toobtain angiography images that could be used to replace fluorescein andindocyanine green angiographies, with the additional advantage of beingintrinsically 3-dimensional rather than 2-dimensional. Additional usescan include, but not be limited to, imaging of blood flow in otherbiological tissue and the imaging of flow in any system, living ornonliving.

In more detail, prior art full-spectrum decorrelation 102 achievesdecorrelation purely through process the amplitude signal and does notrequire phase information. To evaluate the flow signals coming from thescattering tissue, an average decorrelation image D(x,z) at eachposition is obtained by averaging N−1 decorrelation image framescomputed from N reflectance amplitude images frames from M-B modescanning. Each decorrelation frame is computed from 2 adjacent amplitudeframes: A_(n)(x,z) and A_(n+1)(x,z). Using the full spectrumdecorrelation method 102, the decorrelation image it is given by thefollowing equation:

$\begin{matrix}{{\overset{\_}{D}\left( {x,z} \right)} = {1 - {\frac{1}{N - 1}{\sum\limits_{n = 1}^{N - 1}{\frac{{A_{n}\left( {x,z} \right)}{A_{n + 1}\left( {x,z} \right)}}{\left\lbrack {{\frac{1}{2}{A_{n}\left( {x,z} \right)}^{2}} + {\frac{1}{2}{A_{n + 1}\left( {x,z} \right)}^{2}}} \right\rbrack}\mspace{14mu} \left( {N = 8} \right)}}}}} & (1)\end{matrix}$

where x and z are lateral and depth indices of the B-scan images and ndenotes the B-scan slice index. In this full spectrum equation, thedecorrelation signal-to-noise ratio acquired from full spectrum can onlybe increased by increasing the number N of B-scans taken at the sameposition. However, more scans require more imaging time which may not bepractical.

In more detail, prior art pixel averaging method 112 can producedecorrelation images given by the following equation:

$\begin{matrix}{{{\overset{\_}{D}\left( {x,z} \right)} = {1 - {\frac{1}{N - 1}\frac{1}{PQ}{\sum\limits_{n = 1}^{N - 1}{\sum\limits_{p = 1}^{P}{\sum\limits_{q = 1}^{Q}\frac{{A_{n}\left( {{x + p},{z + q}} \right)}{A_{n + 1}\left( {{x + p},{z + q}} \right)}}{\left\lbrack {{\frac{1}{2}{A_{n}\left( {{x + p},{z + q}} \right)}^{2}} + {\frac{1}{2}{A_{n + 1}\left( {{x + p},{z + q}} \right)}^{2}}} \right\rbrack}}}}}}}\mspace{79mu} \left( {{P = 1},{Q = 4},{N = 8}} \right)} & (2)\end{matrix}$

where P and Q are the averaging window widths in the X and Z directions,as described in .J. Enfield, E. Jonathan, and M. Leahy, “In vivo imagingof the microcirculation of the volar forearm using correlation mappingoptical coherence tomography (cmoct),” Biomed. Opt. Express 2(5),1184-1193 (2011). To suppress the spurious noise and discontinuities inthe vasculature, P by Q window moving average can be implemented overthe X-Z 2D map. To fairly compare the prior art pixel averaging method112 with the split-spectrum decorrelation 122 described herein, a 1 by 4window can be created, which means pixel-averaging is only applied alongthe Z direction, the same direction used for splitting the spectrum insplit-spectrum decorrelation 122

In more detail, split-spectrum decorrelation 122 can producedecorrelation images given by the following equation:

$\begin{matrix}{{{\overset{\_}{D}\left( {x,z} \right)} = {1 - {\frac{1}{N - 1}\frac{1}{M}{\sum\limits_{n = 1}^{N - 1}{\sum\limits_{m = 1}^{M}\frac{{A_{n}\left( {x,z} \right)}{A_{n + 1}\left( {x,z} \right)}}{\left\lbrack {{\frac{1}{2}{A_{n}\left( {x,z} \right)}^{2}} + {\frac{1}{2}{A_{n + 1}\left( {x,z} \right)}^{2}}} \right\rbrack}}}}}}\mspace{14mu} \mspace{79mu} \left( {{M = 4},{N = 8}} \right)} & (3)\end{matrix}$

After splitting the spectrum by applying M (for example, M can=4 asdescribed in an exemplary example below) equally spaced bandpassfilters, M individual decorrelation images can be obtained between eachpair of B-scans, which can then be averaged along both the lateral (X)and axial (Z) directions to increase DSNR. In split-spectrumdecorrelation 122, the average decorrelation image D(x, z) can bedescribed as the average of decorrelation images from M spectral bands.By increasing the number M (up to a point), the decorrelationsignal-to-noise ratio can be improved without increasing the scanacquisition time.

Whichever decorrelation method is used (full-spectrum 102,pixel-averaging 112, and split-spectrum 122) the resulting averagedecorrelation image frame D(x,z) should be a value between zero and one,indicating weak and strong decorrelation, respectively. By describingthe decorrelation methods in such detail above, it is possible tocompare the methods to one another based on the resulting decorrelationimages obtained as illustrated in chart 100 of FIG. 1. Thesplit-spectrum method 122 suppresses noise from axial bulk motion and,in addition, makes use of information in the full k spectrum resultingin superior decorrelation signal-to-noise ratio for flow detection.Utilizing the split-spectrum method 122, axial bulk motion can besuppressed by the use of spectral (k) bandpass filters that increase theaxial dimension of the resolution cell so that it can be equal (orsubstantially equal) to the transverse dimension of the resolution cell.

Illustrated in FIG. 2 is diagram 200 visually depicting modification ofan OCT imaging resolution cell 202 via two distinct and separatetechniques (band-pass filtering 204 and split-spectrum 206) to create anisotropic resolution cell 208. Each pixel in a B-scan OCT image isformed from backscattered signals of a 3D volume in space, referred toas a resolution cell (e.g., imaging resolution cell 202 in FIG. 2). Thestatistical changes in the envelope intensity are related to the motionof scatterers through the OCT resolution cell. For a typicalswept-source OCT setup, the axial (Z direction) resolution, determinedby the source central wavelength and its spectral bandwidth, is muchhigher than the lateral resolution determined by the laser beam profilein both X and Y directions. For example, in common swept source OCTsystems, using the full-width-half-maximum (FWHM) amplitude profiledefinition, the axial resolution (˜5 μm) is four times higher than thelateral resolution (˜18 μm) if both are defined asfull-width-half-maximum amplitude profiles (e.g., imaging resolutioncell 202 depicts x=y=4z). This anisotropic resolution cell, with higheraxial than transverse resolution, will result in higher decorrelationsensitivity for axial motion. In the fundus, ocular pulsation related toheart beat, driven by the retrobulbar orbital tissue, mainly occursalong the axial direction. The anisotropic resolution cell of retinalOCT imaging is very sensitive to this axial motion noise. On the otherhand, retinal and choroidal blood flow vectors are primarily transverseto the OCT beam, along the wider (less sensitive) dimensions of the OCTresolution cell. Therefore, to improve the signal-to-noise ratio (SNR)of flow detection, it is desirable to lower the axial resolution anddampen the axial decorrelation sensitivity. This reduces the axialmotion noise without sacrificing the transverse flow signal.

One straightforward way to achieve this resolution modification isband-pass filtering of the spectral interferogram (e.g., band-passfiltering 204). Unfortunately, this also sacrifices most of the speckleinformation in the spectral interferogram and decreases the flow signal.Thus, this is not an effective way to increase the SNR of flow(decorrelation) detection. A better way to decrease axial resolutionwithout losing any speckle information is to split the spectrum intodifferent frequency bands (e.g., split-spectrum 206) and calculatedecorrelation in each band separately. The decorrelation (flow) imagesfrom the multiple spectral bands can then be averaged together to makefull use of the speckle information in the entire OCT spectrum. Thedetails of the split-spectrum procedure are explained herein and below(e.g., split-spectrum decorrelation 122 of FIG. 1 can be utilized).

Illustrated in FIG. 3 is a visual 300 of one 3D volumetric data 302comprising data obtained via an exemplary embodiment M-B-scan mode froman OCT system. N consecutive B-scans at a single Y position compriseM-B-scan 306 (e.g., in some exemplary embodiments described herein,N=eight (8), but is not limited to any specific N). Therefore, for each3D volumetric data 302, in the fast scan (x) axis, a single B-scancomprises a plurality of A-scans 304, and in the slow scan (y) axis,there are a number of M-B-scans 306 comprising N consecutive B-scans.

FIG. 4 shows an exemplary method 400 for creating a decorrelation (flow)image that uses split-spectrum techniques and the full information inthe entire OCT spectral range. The method 400 can be performed, forexample, by in vivo imaging systems described herein below. Portions ofmethod 400 and any of the other methods (or portion of methods)described herein can be performed by computer-executable instructionsstored on computer-readable media and articles of manufacture for invivo imaging.

At 402, M-B scans of OCT spectrum are received. For example, M-B scansas depicted in visual 300 of FIG. 3 can be received from an OCT in vivoimaging system.

At 404, M spectral bands can be created from the M-B scans of OCTspectrum 402. For example, split spectrum 206 of FIG. 2 can be utilizedto create the M spectral bands.

At 406, averaged decorrelation images for each spectral band of the Mspectral bands can be created. For example, split spectrum decorrelation122 described in FIG. 1 can be utilized to create decorrelation imagesfor the M spectral bands and then for each spectral band thosedecorrelation images can be averaged.

At 408, the averaged decorrelation images for each spectral band createdat 406 can be averaged to create a single final image (e.g., finaldecorrelation image) 410.

FIG. 5 shows additional exemplary methods 500, including reference tosimilar methods within method 400 of FIG. 4, for creating adecorrelation (flow) image that uses split-spectrum techniques and thefull information in the entire OCT spectral range. The method 500 can beperformed, for example, by in vivo imaging systems described hereinbelow. Portions of method 500 and any of the other methods (or portionof methods) described herein can be performed by computer-executableinstructions stored on computer-readable media and articles ofmanufacture for in vivo imaging.

FIG. 6 schematically illustrates via visual 600 a 2D spectralinterferogram split into different frequency bands as described inmethods 400 of FIGS. 4 and 500 of FIG. 5.

FIG. 7 schematically illustrates via visual 700 the methods 400 of FIGS.4 and 500 of FIG. 5 for creating a decorrelation (flow) image that usessplit-spectrum techniques and the full information in the entire OCTspectral range.

FIG. 8 is a flowchart 800 showing an exemplary method for eliminatingdecorrelation images with excessive motion noise (e.g., as described inmethod 500 of FIG. 5).

Continuing with the method 500 of FIG. 5, at 502, M-B scans of OCTspectrum are received. For example, M-B scans of OCT spectrum 402 can bereceived from an OCT in vivo imaging system, as depicted in FIG. 7. Inmore detail, for example, spectral interference signal recorded by ahigh speed digitizer in swept-source OCT, after subtracting backgroundand autocorrelation terms, can be received and simply given by thefollowing equation

I(x,k)=∫_(−∞) ^(∞) R(k)A(x,k,z)cos(2kz)dz  (4)

where x is the transverse position of focus beam spot on the samplealong the fast scan axis, k is the wavenumber, I(x,k) is the lightintensity, R(k) is the amplitude of light reflected from the referencearm, A(x,k,z) is the amplitude of the light backscattered from thesample, and z is the optical delay mismatch between the samplereflections and the reference reflection in the free space equivalent.

At 504, overlapping filters (M) covering the entire spectrum can becreated. Additionally, at 506, band pass filtering along k can beconducted. Collectively, creating overlapping filters 504 and band pastfiltering 506 can result in creating M spectral bands 507 as depicted inFIG. 7 (e.g., as described in creating M spectral bands 404 in method400 of FIG. 4). Following along with the example provided above of thespectral interference signal represented by equation (4), the Gaussianshape above the 2D interferogram I(x,k) (e.g., 2D interferogram 605 ofFIG. 6) can be used to express the received interferometric fringe atone position. The bandwidth of this full-spectrum fringe can first bedefined, and then a filter bank created to divide this full-spectrumfringe into different bands (e.g., creating overlapping filters (M) 504of method 500). The specifications of this filter bank can depend onseveral factors, including, but not limited to, 1) filter type, 2)bandwidth of each filter, 3) overlap between different bands, and 4)number of bands. In one exemplary embodiment, a Gaussian filter can beintroduced whose function was defined by the following equation:

$\begin{matrix}{{G(n)} = {\exp\left\lbrack {- \frac{\left( {n - m} \right)^{2}}{2\; \sigma^{2}}} \right\rbrack}} & (5)\end{matrix}$

where n is the spectral element number that varies from 1 to 1400 and islinearly mapped to wavenumber k. The range of sampled k can be 10000 to9091 cm⁻¹, corresponding to a wavelength range of 1000 to 1100 nm. Thebandwidth, referred to as “BW,” (e.g., as depicted in 604 of FIG. 6) ofthe full spectrum can be 69 nm, which can provide a FWHM axial spatialresolution of 5.3 μm. m is the position of the spectral peak. In anexemplary embodiment, the peaks of the spectral Gaussian filters can beplaced at 9784, 9625, 9466, and 9307 cm⁻¹. σ² is the variance of theGaussian filter in terms of the number of spectral elements. In anexemplary embodiment, the FWHM amplitude bandwidth, referred to as “bw,”of the bandpass filters can equal to 2√{square root over (2 ln 2)}σ,covering 378 spectral elements, corresponding to a wavelength range of27 nm or a wavenumber range of 245 cm⁻¹. The four (4) bandpass filters(e.g., as depicted in 608, 610, 612, and 614 of FIG. 6), described insuch an exemplary embodiment, can overlap so that none of the frequencycomponents of the original signal are lost in the processing. FIG. 6visually displays a 2D spectral interferogram 605 split at 606 (e.g.,via 404 of method 400 of FIG. 4) into four new spectra with smaller kbandwidth, with “BW” 604 indicating the bandwidth of a full-spectrumfilter and multiple “bw”s 608, 610, 612, and 614 being the bandwidth ofmultiple Guassian filters, respectively, and regions of non-zero valuesin the data block are indicated by the dark shaded patterns 616, 618,620, and 622 (similarly visually depicted, for example in FIG. 7).

At 508, the M spectral bands 507 from each individual frequency band canbe passed into conventional Fourier-domain OCT algorithms to Fouriertransform along k. Additionally, phase information can be dropped toresult in amplitude information for each spectral band 509 (e.g., asdepicted in FIG. 7). For example, the OCT signals therefore can bedirectly calculated from the decomposed interferograms I′(x, k) byapplying Fourier transform upon wavenumber k. The computed OCT signalcan be a complex function, Ĩ(x, z), which can be written as thefollowing equation:

Ĩ(x,z)=FFT{I′(x,k)}=A(x,z)exp[iφ(x,z)]  (6)

where φ(x,z) is the phase of the analytic signal Ĩ(x,z). The amplitudesof the OCT signals, A(x,z), can be used while the phase information canbe selectively disregarded.

At 510, a fixed value can be set for removal of high decorrelationgenerated by background noise. Decorrelation of OCT signal amplitudebetween B-scans taken at the same nominal position can be caused byseveral sources: (1) flow, (2) bulk tissue motion or scanner positionerror, and (3) background noise. To help accentuate true flowmeasurement in the images created and improve the signal-to-noise ratiofor flow detection, removal of high decorrelation generated bybackground noise is desirable. Background noise is random and thereforehas high decorrelation between B-scan frames. Noise predominates inpixels with low OCT signal amplitude and therefore flow cannot beassessed in these pixels with any accuracy. A fixed decorrelation valueof zero (0) can be assigned to these pixels with low OCT signalamplitude. For example, this can be achieved by setting the low signalpixels a constant amplitude value. The threshold value, for example, canthen be chosen to be two standard deviations above the mean backgroundvalue measured when the sample beam was blocked.

At 512, decorrelation calculation can be obtained between adjacentamplitude frames. For example, split-spectrum decorrelation 122 asdescribed in FIG. 1 can be utilized to produce decorrelation images foreach spectral band 513 (e.g., as depicted in FIG. 7 visually).

At 514, decorrelation images for each spectral band 513 having excessivemotion noise can be eliminated. To help accentuate true flow measurementin the images created and improve the signal-to-noise ratio for flowdetection, removal of decorrelation generated by bulk tissue motion orscanner position is desirable. Saccadic and micro-saccadic eye movementsare rapid and cause a high degree of decorrelation between B-scans, asdepicted, for example, in flowchart 800 of FIG. 8. Such movements can beseen in visual 802 which displays three frames of a set of seven (7)decorrelation images 804 (Dn) of the region around the optic nerve head(ONH), computed from eight (8) OCT B-scans at the same Y location. Eachdecorrelation image frame depicted can be calculated from a pair ofadjacent B-scan amplitude frames, for example as described using themethods described above. In six (6) of the seven (7) decorrelationframes, flow pixels could be distinguished from non-flow pixels by theirhigher decorrelation values. However, in frame D4 806, both flow(vessel) and non-flow (bulk tissue) pixels had high decorrelation valuespossibly due to rapid eye movement (e.g., saccadic). To detect bulkmotion, the median decorrelation value in the highly reflective portionof the imaged tissue structures (between the region noted as 808) can bedetermined. High bulk motion in frame D4 806 can be detected by highmedian decorrelation value in pixel histogram analysis 810. Histogramanalysis can be performed within a high reflectivity band starting atthe retinal inner limiting membrane and spanning 30 pixels below (withinregion 808 of 802. By comparing the median decorrelation value 814 to apreset threshold 812 (e.g., in one exemplary embodiment the thresholdwas set at 0.15, two standard deviations above the median decorrelationvalue), it can be determined that a frame (e.g. frame D4) is astatistical outlier and should be eliminated. Visual 816 depicts theresult after the removal of the outlier frame D4.

At 516, the decorrelation images at each spectral band that remain afterimages with excessive motion noise have been removed can be averaged tocreate an average decorrelation image for each spectral band, thereforeresulting in multiple averaged decorrelation images (i.e., one averagedecorrelation for each spectral band as visualized in FIG. 7).

At 518, the averaged decorrelation images from M spectral bands areaveraged to create one final decorrelation image 410 (e.g., asvisualized in FIG. 7 and also described in method 400, step 408 of FIG.4).

Returning back to flowchart 800 of FIG. 8, after removing frame D4 806as an outlier, the remaining six (6) decorrelation images can beaveraged to produce a cleaned decorrelation image 818 which displayshigh contrast between flow pixels (e.g., bright area in retinal vesselsand choroid) and non-flow dark regions. An uncleaned decorrelation image820 depicts a final decorrelation image had outlier frame D4 806remained showing less contrast between flow (vessels) and non-flow(static tissue) pixels compared to the cleaned decorrelation image 818,as evident by the lack of completely dark space between retinal vesselsin the peripapillary areas circled at 822 and 824.

Utilizing method 500, a 3D dataset comprising a stack of two hundred(200) corrected average decorrelation cross-sectional images, along withthe associated average reflectance images, that together spans 3 mm inthe slow transverse scan (Y) direction can be obtained. In someembodiments it may be desirable to separate the 3D data into retinal andchoroidal regions with the dividing boundary set at the retina pigmentepithelium (RPE). The depth (Z position) of the highly reflective RPEcan be identified through the analysis of the reflectance andreflectance gradient profiles in depth. The region above the RPE is theretinal layer and the region below is the choroidal layer. The en faceX-Y projection angiograms can then be produced by selecting the maximumdecorrelation value along the axial (Z) direction in each layer. In ONHscans, the RPE depth just outside the disc boundary can be used to setan interpolated RPE plane inside the disc.

FIG. 9 schematically illustrates an in vivo imaging system 900 forcollecting OCT image information. For example, a high-speed swept-sourceOCT system 900 (e.g., as described in B. Potsaid, B. Baumann, D. Huang,S. Barry, A. E. Cable, J. S. Schuman, J. S. Duker, and J. G. Fujimoto,“Ultrahigh speed 1050 nm swept source/fourier domain oct retinal andanterior segment imaging at 100,000 to 400,000 axial scans per second,”Opt. Express 18(19), 20029-20048 (2010)) can used to demonstrate themethods described above for imaging of microcirculation in the humanocular fundus. High speed swept-source OCT system 900 comprises atunable laser 901. For example, tunable laser 901 (e.g., a tunable laserfrom Axsun Technologies, Inc, Billerica, Mass., USA) may have awavelength of 1050 nm with 100 nm tuning range, a tuning cycle with arepetition rate of 100 kHz and a duty cycle of 50%. Such OCT system 900can produce a measured axial resolution of 5.3 μm(full-width-half-maximum amplitude profile) and an imaging range of 2.9mm in tissue. Light from swept source 901 can be coupled into a two bytwo fiber coupler 902 through single mode optical fiber. One portion ofthe light (e.g., 70%) can proceed to the sample arm (i.e., the patientinterface), and the other portion of the light (e.g., 30%) can proceedto the reference arm.

In the sample arm, a sample arm polarization control unit 903 can beused to adjust light polarization state. The exit light from the fibercoupler 902 can then be coupled with a retinal scanner whereby the lightis collimated by sample arm collimating lens 904 and reflected by mirror905 and two dimensional galvo scanner 909 (e.g., an XY galvonanometerscanner). Two lenses, first lense 906 (e.g., an objective lense) andsecond lense 907 (e.g., an ocular lense) can relay probe beam reflectedby galvo scanner 909 into a human eye 908. For example, a focused spotdiameter of 18 μm (full-width-half-maximum amplitude profile) can becalculated on the retinal plane based on an eye model. The average lightpower (i.e., output power of the laser) onto a human eye can be 1.9 mW,which is consistent with safe ocular exposure limit set by the AmericanNational Standard Institute (ANSI).

The reference arm can comprise a first reference arm collimating lens913, a water cell 912, a retro-reflector 911, a glass plate 914 and asecond reference arm collimating lens 915. Glass plate 914 can be usedto balance the dispersion between the OCT sample arm and reference arm.Water cell 912 can be used to compensate the influence of dispersion inthe human eye 908. Retro-reflector 911 can be mounted on a translationstage 910 which can be moved to adjust the path length in the referencearm.

Light from the sample and reference arm can interfere at beam splitter917. A reference arm polarization control unit 916 can be used to adjustthe beam polarization state in the reference arm to maximum interferencesignal. The optical interference signal from beam splitter 917 (e.g., a50/50 coupler) can be detected by a balanced detector 918 (e.g., abalanced receiver manufactured by Thorlabs, Inc, Newton, N.J., USA),sampled by an analog digital conversion unit 919 (e.g., a high speeddigitizer manufactured by Innovative Integration, Inc.) and transferredinto computer 920 for processing. For example, computer 920 can be usedfor storing instructions for, and implementing, the methods describedherein. Interference fringes, for example, can be recorded by analogdigital conversion unit 919 at 400 MHz with 14-bit resolution, with theacquisition driven by the optical clock output of tunable laser 901. Insuch an exemplary setup, imaging system 900, sensitivity can be measuredwith a mirror and neutral density filter at 95 dB, with a sensitivityroll-off of 4.2 dB/mm.

While a swept-source OCT system has been described above, the technologydisclosed herein can be applied to any Fourier-domain OCT system. InFourier-domain OCT systems the reference mirror is kept stationary andthe interference between the sample and reference reflections arecaptured as spectral interferograms, which are processed byFourier-transform to obtain cross-sectional images. In the spectral OCTimplementation of Fourier-domain OCT, a broad band light source is usedand the spectral interferogram is captured by a grating or prism-basedspectrometer. The spectrometer uses a line camera to detect the spectralinterferogram in a simultaneous manner. In the swept-source OCTimplementation of Fourier-domain OCT, the light source is a laser thatis very rapidly and repetitively tuned across a wide spectrum and thespectral interferogram is captured sequentially. Swept-source OCT canachieve higher speed and the beam can be scanned transversely morerapidly (with less spot overlap between axial scans) without sufferingas much signal loss due to fringe washout compared to otherFourier-domain OCT systems. However, a very high speed spectral OCTsystem could also be utilized with the technology disclosed herein.

Any one of the various embodiments as discussed may be incorporated incombination with multiple rings circular scan Doppler OCT to providetotal retinal blood flow (TRBF) measurement for both veins and arteriesaround the optic disc. Utilizing faster OCT systems, higher velocitiesof flow in arteries (and veins) can be within detectable range and morecircles (e.g., rings) can be scanned within a fraction of the cardiaccycle time. By utilizing a scan pattern of multiple rings (e.g., four(4) or more circular scans) the calculation of vessel curvature andslope relative to the OCT beam axis can be obtained. These calculationscan allow for more accurate calculation of Doppler angles in the highlycurved segments of retinal vessels as they emerge from the optic headnerve (OHN.) The circles can be closer to the ONH, where the Dopplerangles are usually larger, obviating the need for a dual-angle scanningprotocol. The OCT beam can then approach at a single angle through thecenter of an undilated pupil. Each scan can therefore yield a valid TRBDmeasurement, so fewer scans will be needed. As a vessel can betransected by four (4) or more circular scans, the crossing andbranching points can be disregarded (in full or part) in favor ofstraight sections where flow measurements can be more reliable. Withfaster scan times per circular scan, motion errors can be reduced aswell. In lieu of color disc photography commonly utilized in combinationwith Doppler OCT, 3D angiography utilizing the techniques discussed(e.g. SSADA) can be used to resolve 3D vessel anatomy.

Any one or more of various embodiments as discussed may be incorporated,in part or in whole, into a system. FIG. 10 illustrates an exemplaryembodiment of an in vivo imaging system (e.g. an OCT system) 1000 inaccordance with various embodiments described herein. In theembodiments, OCT system 1000 may comprise an OCT apparatus 1002 and oneor more processors 1012 coupled thereto. One or more of the processors1012 may be adapted to perform methods in accordance with variousmethods as disclosed herein. In various embodiments, OCT system 1000 maycomprise a computing apparatus including, for example, a personalcomputer in any form, and in various ones of these embodiments, one ormore of the processors may be disposed in the computing apparatus. OCTsystems in accordance with various embodiments may be adapted to storevarious information. For instance, an OCT system may be adapted to storeparameters and/or instructions for performing one or more methods asdisclosed herein.

In various embodiments, an OCT system may be adapted to allow anoperator to perform various tasks. For example, an OCT system may beadapted to allow an operator to configure and/or launch various ones ofthe above-described methods. In some embodiments, an OCT system may beadapted to generate, or cause to be generated, reports of variousinformation including, for example, reports of the results of scans runon a sample.

In embodiments of OCT systems comprising a display device, data and/orother information may be displayed for an operator. In embodiments, adisplay device may be adapted to receive an input (e.g., by a touchscreen, actuation of an icon, manipulation of an input device such as ajoystick or knob, etc.) and the input may, in some cases, becommunicated (actively and/or passively) to one or more processors. Invarious embodiments, data and/or information may be displayed, and anoperator may input information in response thereto.

Any one or more of various embodiments as discussed may be incorporated,in part or in whole, into an article of manufacture. In variousembodiments and as shown in FIG. 11, an article of manufacture 1100 inaccordance with various embodiments described herein may comprise astorage medium 1112 and a plurality of programming instructions 1102stored in storage medium 1112. In various ones of these embodiments,programming instructions 1102 may be adapted to program an apparatus toenable the apparatus to perform one or more of the previously-discussedmethods.

In various embodiments, an OCT image may provide data from which adiagnosis and/or evaluation may be made. In embodiments, suchdeterminations may relate to biologic tissue structure, vasculature,and/or microcirculation. For example, in some embodiments, 3-D in vivoimaging of a biologic tissue and quantifying flow of blood throughindividual vessels therein may be useful in understanding mechanismsbehind a number of disease developments and treatments including, forexample, ischemia, degeneration, trauma, seizures, and various otherneurological diseases. In still other embodiments, an OCT image andtechniques herein disclosed may be used to identify cancer, tumors,dementia, and ophthalmologic diseases/conditions (including, e.g.,glaucoma, diabetic retinopathy, age-related macular degeneration). Stillfurther, in various embodiments, OCT techniques as herein disclosed maybe used for endoscopic imaging or other internal medicine applications.The foregoing illustrative embodiments of diagnosis and/or evaluationare exemplary and thus embodiments described herein are not limited tothe embodiments discussed.

Although certain embodiments have been illustrated and described hereinfor purposes of description, it will be appreciated by those of ordinaryskill in the art that a wide variety of alternate and/or equivalentembodiments or implementations calculated to achieve the same purposesmay be substituted for the embodiments shown and described. Those withskill in the art will readily appreciate that embodiments describedherein may be implemented in a very wide variety of ways.

EXEMPLARY EMBODIMENTS A. Certain Embodiments

Macular and ONH imaging were performed on three normal volunteers usinga swept-source OCT system 900 described herein, as approved by anInstitutional Review Board (IRB). For each imaging, the subject's headwas stabilized by chin and forehead rests. A flashing internal fixationtarget was projected by an attenuated pico projector using digital lightprocessing (DLP) technology (Texas Instruments, Dallas, Tex., USA). Theimaging area on the fundus was visualized by the operator usingreal-time en face view of a 3 mm×3 mm OCT preview scan.

The swept-source OCT system was operated at 100-kHz axial scanrepetition rate. In the fast transverse scan (X) direction, the B-scanconsisted of 200 A-scans over 3 mm. In the slow transverse scan (Y)direction, there were 200 discrete sampling planes over 3 mm. Eightconsecutive B-scans were acquired at each Y position. This is referredto as the “M-B-scan mode” (e.g., as illustrated in FIG. 3) because itenables detection of motion between consecutive B-scans at the sameposition. Thus, it took 3.2 sec to obtain a 3D volumetric data cubecomprised of 1600 B-scans and 32,0000 A-scans. Under this scanningprotocol, methods described herein were applied to the repeated framesequences at each step. Finally, the 200 calculated B-scan frames werecombined to form 3D blood perfusion images of posterior part of thehuman eye.

FIG. 12 illustrates in vivo 3-D volumetric structure images (3.0 (x)×3.0(y)×2.9 (z) mm) of the optic nerve head (ONH) in the right eye of amyopic individual using imaging methods in accordance with variousembodiments described herein. From one 3D volumetric dataset, bothreflectance intensity images and decorrelation (angiography) images wereobtained. For the optical nerve head (ONH) scan, the en face maximumprojection of reflectance intensity 1202 showed the major retinal bloodvessels and the second order branches 1204, but finer branches and themicrocirculation of the retina, choroid, and optic disc were notvisible. In the vertical cross-sectional intensity image 1208 taken fromplane 1206 of projection 1202, the connective tissue struts (bright) andpores (dark) of the lamina cribosa could be visualized deep within theoptic disc. Around the disc, the retina, choroid, and sclera can bedelineated. The ONH angiogram obtained by the methods described hereinshowed both many orders of vascular branching as well as themicrocirculatory network. The en face maximum decorrelation projectionangiogram 1210 showed many orders of branching from the central retinalartery and vein, a dense capillary network in the disc, a cilioretinalartery (reference by an arrow in angiogram 1210 at the nasal discmargin), and a near continuous sheet of choroidal vessels around thedisc, much of which could not be visualized well on the en faceintensity image 1202. The vertical SSADA cross-section decorrelationimage 1212 (in the same plane 1206 as 1208 displayed) created showedblood flow in blood vessels in the disc (represented by arrows), retinalvessels, and choroid that form columns from the surface to a depth of˜1.0 mm. It may be unclear if this represents deep penetrating vesselsor if this is represents decorrelation projection artifact. Projectionartifact refers to the fact that light reflected from deeper staticstructures may show decorrelation due to passing through a moresuperficial blood vessel. This type of artifact is evident where theperipapillary retinal vessels seem thicker than they should be, forexample in fly-through movie still frame image 1216 and in decorrelationimage 1212. Due to this artifact, these vessels extended down the fulldepth of the nerve fiber layer (NFL), and the decorrelation signalappeared in the subjacent pigment epithelium (RPE), which should beavascular.

To separately view the retinal vessels and superficial disc vessels,pixels were removed below the level of the peripapillary RPE to removethe choroid. The resulting en face angiogram 1214 showed that thesuperficial vascular network nourishes the disc ends at the discboundary. By comparison, the choroidal circulation formed an almostcontinuous sheet of blood flow under the retina as shown in 1210. The enface images 1202, 1210, and 1214 show RPE atrophy in a temporal crescentjust outside the disc margin. Inside the crescent there was also a smallregion of choriocapillaris atrophy (see the arrow region within 1210).Overlaying the cross-sectional gray scale reflectance intensity imagewith the color scale flow (decorrelation) image showed that the majorretinal branches vessels were at the level of the peripapillary NFL, asshown in fly-through movie still frame image 1216 (i.e., how the disc,retina, and choroid are perfused in a 3D volumetric fashion). It alsoshowed the blood flow within the full thickness of the choroid. Thecombined image 1216 also showed that the deeper disc circulation residesprimarily in the pores of the lamina cribosa and not in the connectivetissue struts. This may be the first time that the disc microcirculationhas been visualized noninvasively in such a comprehensive manner. Thehorizontal line across the image was a result of a fixed patternartifact that originated from the swept laser source.

Another exemplary embodiment disclosed herein was demonstrated inmacular angiography. The macular region of the fundus is responsible forcentral vision. Capillary dropout in the macular region due to diabeticretinopathy is a major cause of vision loss. Focal loss of thechoriocapillaris is a possible causative factor in the pathogenesis ofboth dry and wet age-related macular degeneration, the leading cause ofblindness in industrialized nations. Thus macular angiography isimportant. The technology described herein was used to demonstratemacular angiography of both the retinal and choroidal circulations in anormal eye as shown in the in vivo 3-D volumetric structure images (3.0(x)×3.0 (y)×2.9 (z) mm) of the macula in FIG. 13.

The vascular pattern and capillary networks visualized using thetechnology disclosed herein were similar to those previously reportedusing phase-based OCT angiography techniques. The flow pixels formed acontinuous microcirculatory network in the retina. There was an absenceof vascular network in the foveal avascular zone (as shown in en facemaximum decorrelation projection angiogram 1302) of approximately 600 μmdiameter, in agreement with known anatomy. There were some disconnectedapparent flow pixels within the foveal avascular zone due to noise.Horizontal OCT cross section through the foveal center (upper dashedline in 1302) with merged flow information (decorrelation represented inbright/color scale) and structure information (reflectance intensityrepresented in gray/darker scale) is represented with foveal centerimage 1304. Inspection of foveal center image 1304 shows these falseflow pixels to be decorrelation noise in the high reflectance layers ofthe RPE and photoreceptors. The choriocapillaris layer forms a confluentoverlapping plexus, so it is to be expected that the projection image ofthe choroid circulation (see en face maximum decorrelation projectionangiogram of the choroidal circulation 1306) shows confluent flow.Similar to 1304, a merged horizontal OCT cross section of the inferiormacula (lower dashed line in 1302) is represented with inferior maculaimage 1308. The cross section images 1304 and 1308 showed retinalvessels from the NFL to the outer plexiform layer, in agreement withknown anatomy. The flow in the inner choroid had higher velocity asbased on decorrelation seen in the bright/color scale. The volume wasalso greater than the retinal circulation (as shown in the cross sectionimages 1304 and 1308), again consistent with known physiology that thechoroidal circulation has much higher flow than the retinal circulation.There were signal voids in the outer choroid which may be due to fringewashout from high flow velocity and the shadowing effect of overlyingtissue. The cross section images 1304 and 1308 also showed a few spotsof decorrelation in the RPE layer. These are likely artifacts becausethe RPE is known to be avascular. As mentioned previously, this islikely due to the projection of decorrelation of flow in a proximallayer (i.e., inner retinal layers) onto distal layers with a strongreflected signal (i.e., RPE). There was also a tendency for vessels toform vertical arrays in the inner retina, which may in some instances bedue to the projection artifact as well.

Another embodiment disclosed herein was demonstrated to appreciate thedifferences between full-spectrum, pixel-averaging, and split-spectrumtechniques (as described in FIG. 1) for decorrelation-based angiography.To obtain angiograms, the methods described above were used, inparticular with reference to FIG. 1 and as described by equations(1)-(3), respectively. For fair comparison, identical motion errorreduction, noise threshold, and en face projection methods were used.

FIG. 14 illustrates en face angiograms of macular retinal circulationusing methods in accordance with prior art methods full-spectrum (1402)and pixel-averaging (1404) and in accordance with various embodimentsdescribed herein (1408). While the prior art methods and those describedherein provided good visualization of major macular vessels, thecapillary network looked the cleanest and most continuous insplit-spectrum angiogram 108 generated with the split-spectrumembodiment. The pixel-averaging method producing pixel-averagingangiogram 1404 displays the second cleanest and continuous capillarynetwork. The full-spectrum method producing full-spectrum angiogram 1402showed significantly more disconnected flow pixels that were likely tobe noise. The noise can be most easily appreciated in the fovealavascular zone (inside the yellow circles of 1402A, 1402B, and 1408Cimages of 600-um diameter), which should not have any retinal vessels,including capillaries. In the split-spectrum angiogram 1408, there was anear continuous visualization of the capillary network just outside theavascular zone, while this loop appeared broken up using the other twoprior art techniques. The cross-sectional angiograms for each method asdisplayed in 1402D, 1404E, and 1408F (all scanned across a horizontaldashed line as shown in 1402A showed that the split-spectrum methodprovided the cleanest contrast between distinct retinal vessels and darkbackground. Again, the pixel-averaging method was second best, and thefull-spectrum method showed visible snow-like background noise.

To obtain quantitative figures of merit to compare the threedecorrelation-based angiography techniques, two pieces of anatomicknowledge were used. One is that the retinal vessels form a continuousnetwork, and the other is that there are no retinal vessels within thefoveal avascular zone. FIG. 15 illustrates in vivo images depictingvascular connectivity and signal to noise ratio (SNR) using methods inaccordance with prior art methods and in accordance with variousembodiments described herein. In FIG. 15, images 1502A1-1502A4 wereobtained using the full-spectrum method (all in row 1502), images1504B1-1504B4 were obtained using the pixel-averaging method (all in row1504), and images 1506C1-C4 were obtained using the split-spectrumtechnology described herein. To assess vessel connectivity, theprojection images (1402A, 1404B, and 1408C of FIG. 14) obtained by thethree different methods were converted to binary images (e.g.,binarized) (as shown in the images in first column 1508 of FIG. 15,images 1502A1, 1504B1, and 1506C1) based on a fixed threshold. Then askeletonizing morphological operation (e.g., skeletonized) was appliedto obtain a vascular network made of 1-pixel wide lines and dots (asshown in the images in second column 1510 of FIG. 15, images 1502A2,1504B2, and 1506C2). Next the unconnected flow pixels were separatedfrom the connected flow skeleton (e.g., filtered to remove unconnectedflow pixels) (as shown in the images in third column 1512 of FIG. 15,images 1502A3, 1504B3, and 1506C3). The vascular connectivity wasdefined as the ratio of the number of connected flow pixels to the totalnumber of flow pixels on the skeleton map. Connectivity was analyzed onthe OCT macular angiograms of six eyes of the three participants (seeTable 1 below). A comparison of the three techniques based on pairedt-tests showed that the split-spectrum technology had significantlybetter connectivity relative to the pixel-averaging (p=0.037) andfull-spectrum (p=0.014) techniques. The split-spectrum technologydisclosed herein reduced the number of unconnected flow pixels (18%) bymore than a factor of 2 when compared with the full-spectrum prior arttechnique (39%).

To compute a signal to noise (SNR) for the decorrelation signal, it wasnecessary to define relevant signal and noise regions. For the macula,fortuitously, the central foveal avascular zone (FAZ) is devoid of bloodvessels, including capillaries. The parafoveal capillary networknourishes the fovea and the loss of these capillaries in diabeticretinopathy is an important mechanism in the loss of vision. Thus theratio of decorrelation value in the parafoveal region relative to theFAZ can be a clean and clinically relevant way to compute SNR. In thefourth column 1512 of FIG. 15, images 1502A4, 1504B4, and 1506C4, showdecorrelation SNR, where the noise region was inside the fovealavascular zone (displayed as inner dotted circles with radius R1) andthe signal region was the parafoveal annulus (as displayed the grayedregion between radius R2 and radius R3). The radius of the FAZ (R1) isapproximately 0.3 mm. Therefore, it was chosen that the central FAZ witha radius of 0.3 mm was the noise region and the annular parafovealregion between 0.65 (R2) and 1.00 mm (R3) radii was the signal region.Therefore, the decorrelation signal-to-noise ratio DSNR can be representusing the following formula:

$\begin{matrix}{{DSNR} = \frac{{\overset{\_}{D}}_{Parafovea} - {\overset{\_}{D}}_{FAZ}}{\sqrt{\sigma_{FAZ}^{2}}}} & (7)\end{matrix}$

where D _(Parafovea) and D _(FAZ) are the average decorrelation valueswithin the parafoveal annulus and FAZ, respectively; and σ_(Faz) ² isthe variance of decorrelation values within the FAZ. These computationswere performed over the en face maximum projection images.

The DSNR was analyzed on the OCT macular angiograms performed on sixeyes of the three participants (see Table 1 below). The paired t-testshowed that the DSNR of the split-spectrum technology was significantlyhigher than the pixel-averaging technique (p=0.034) and thefull-spectrum technique (p=0.012). The split-spectrum technologyimproved the DSNR by more than a factor of 2 compared to thefull-spectrum technique.

TABLE 1 Vascular Connectivity and Signal-to-Noise Ratio of ThreeAngiography Algorithms Improve- Connec- ment of Improve- Amplitudetivity connec- DSNR ment of decorrelation (mean ± sd) tivity (mean ± sd)DSNR full-spectrum 0.61 ± 0.08 N/A 3.30 ± 0.81 N/A pixel-averaging 0.70± 0.06 14.8% 4.57 ± 1.08 38.5% split-spectrum 0.82 ± 0.07 34.4% 6.78 ±0.82  105% DSNR = decorrelation signal-to-noise ratio. Statisticalanalysis is based on 6 eyes of 3 normal human subjects.

Utilizing the technology disclosed, visualization of both larger bloodvessels and the capillary network in the retinal and choroidalcirculations has been demonstrated. This visualization can also beenachieved using Doppler and other phase-based flow detection techniques,however the SSADA (i.e., the split-spectrum) techniques disclosed haveseveral potential advantages over phase-based techniques. Insensitivityto phase noise is one advantage. Another advantage includes the abilityto quantify microvascular flow. Because the effective resolution cell ismade isotropic (having the same size in X, Y, and Z dimensions, asdescribed in FIG. 2), it is equally sensitive to transverse (X, Y) andaxial (Z) flow. This contrasts with all phase-based techniques, whichare intrinsically more sensitive to flow in the axial direction overwhich Doppler shift occurs. Thus utilizing the technology disclosedresults in the decorrelation value as a function of the flow velocityregardless of direction. The faster blood particles move across thelaser beam, the higher the decorrelation index of the received signalswithin a velocity range set by the scan parameters. In theory thesaturation velocity should be approximately the size of the resolutioncell (0.018 mm) divided by the interframe time delay (0.002 sec), or 9mm/sec. The minimum detectable flow velocity can be determined by thedecorrelation noise floor, which can be based on the decorrelationdistribution statistics of the non-flow tissue voxels. In this example,the projection view of split-spectrum technology showed the vascularpattern within the macular capillary zone (parafoveal region). Thisdescribes that the split-spectrum technology disclosed is able to detectretinal capillary flow, which is within the range of 0.5-2 mm/sec.Calibration of velocity to decorrelation values using in vitro flowphantom experiments can be done to further determine the minimumdetectable flow velocity.

The projection of flow from proximal (shallower) layers to distal(deeper) layers can be challenging. Flow in the major peripapillaryretinal arteries and veins (as shown in FIG. 12) and larger macularvessels in the inner retina (as shown in FIG. 13) projects onto thehighly reflective RPE, which should not contain any blood vessels. Therewere also probable projection of flow from the more superficial innerretinal layers (i.e. nerve fiber layer and ganglion cell layer) to thedeeper inner retinal layers (i.e. inner and outer plexiform layers).This does not affect the accuracy of en face projection of the retinalcirculation, but it could affect the accuracy of cross-sectionalangiograms and en face projection of the choroidal circulation. One canraise the threshold decorrelation value for flow identification indeeper voxels if a more superficial voxel has a suprathresholddecorrelation value; however, this can inevitably introduce a potentialshadow artifact in place of a flow projection artifact. Thus, images ofdeeper vessels can be interpreted with this artifact in mind.

Noise from bulk tissue motion, while dramatically reduced using thetechnology disclosed herein, may not be entirely eliminated. Asdescribed in the examples disclosed, no attempt was made to compensatefor X-Z motion between consecutive B-scan frames by the use offrame-shift registration. This registration can likely reduce the effectof bulk motion in the X-Z dimensions (though not in the Y direction) andimprove the accuracy of flow detection further. It is also apparent fromthe en face angiograms that there are saccadic motion artifacts in the3D dataset. This can likely be reduced by the use of 3D registrationalgorithms.

FIG. 16 illustrates in vivo images using methods in accordance withvarious embodiments described herein, including a multiple ringscircular scan pattern (e.g. a ring number of 4 and a diameter of 1.6-2.2mm) and Doppler angle estimation for a single vessel. A vessel centerline can be constructed based on vessel center position with splinecurve fitting. The flow vector at each circle can be estimated using thetangential vector of vessel center line. The multiple ring circular scancan be done with an ultrahigh-speed OCT.

The scan pattern can include multiple concentric circular scans with 4or more diameters. At each location (e.g., diameter), the circular scancan be repeated to cover the cardiac cycle and reduce the effect of eyemovement. The total scan time can cover at least one cardiac cycle. Theaxial scan density can be high for precise Doppler shift signalcalculation.

After the scan is obtained, the vessel location can be detected on eachframe for each vessel. For a particular vessel, the center positions onthe scans can be used to reconstruct a vessel center line curve. Thecurve can be used to estimate the Doppler angle between OCT beam andvessel normal vector (as shown FIG. 16). The angle θ can be calculatedbetween the OCT beam and the flow vector, which is the tangential vectorof the vessel center line at vessel center. Then, the Doppler angle canbe estimated as 90-θ.

With the angle and Doppler shift signal, the blood flow can be estimatedby the following equation,

$\begin{matrix}{{Flow} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}{\underset{{vessel}\mspace{14mu} {area}}{\int\int}\frac{\lambda \; \Delta \; f_{i}{x}{z}}{2\; n\; s\; {\ln \left( \alpha_{i} \right)}}}}}} & (8)\end{matrix}$

where, λ: is center wavelength of the OCT laser source, Δfi is Dopplershift signal in ith circular scan, n is refractive index, α is Dopplerangle on ith circular scan, which is equal to 90-θ, θ is the anglebetween vessel vector and OCT beam on ith circular scan. When theDoppler angle is close to 0, it can be difficult to detect the vesselposition automatically. To solve this problem, an OCT angiography scancan be added into the multiple ring circular scan. For each position(diameter), except the single circular scan which creates Doppler OCTimage, multiple circular scans with less axial-scan density can be donejust before or after the Doppler scan and the angiography techniquesdescribed herein (e.g., SSADA) can be applied to get the OCT angiograph.The Doppler scan and angiography scan can be registered, as they aredone at the same location, and eye movement should be small in suchshort period (<0.1 seconds). Then, vessel positions and regions detectedon the angiograph scan can be mapped to the Doppler scan.

In order to get precise flow vector estimation, the interval of circlediameter can be reduced. This can involve increasing the number ofcircles and therefore the total scan time would increase. In clinicalpractice, the total scan time is desired to be about 2 seconds.Therefore there is a compromise between scan time and the accuracy ofangle estimation in multiple ring circular scanning. To solve thisproblem, flow vector from a 3D OCT angiograph can be obtained. The 3Dangiograph scan can cover the Doppler scan area. The vessel position canbe manually or automatically detected on the 3D angiograph and the 3Dvessel structure around the optic disc can be reconstructed. Thecircular Doppler scan and/or circular angiograph scan can be registeredto the 3D angiograph scan. An automatic rigid registration based on thevessel pattern and inner limited membrane can be utilized. After theregistration, the 3D vessel structure can be mapped to the Doppler scanfor vessel detection and angle estimation.

For example, on a swept-source OCT with 100,000 ascan/second scan rate,a four ring circular scan pattern was implemented. The scan diameterutilized was 2.2 mm, 2.0 mm, 1.8 mm and 1.6 mm. 8 fast circular scans(500 A scans) followed by 1 slow circular scan (4000 A scans) werescanned on each diameter. Then the scans on 4 diameters were repeated 6times. Together, the scans took about 2 seconds, within the desiredclinical timeframe. The faster scans were used to calculate the OCTangiograph and the slow scan was used to calculate the Doppler shiftsignal. A 3×3 mm 3D OCT angiography was also obtained. FIGS. 17 A-Dillustrates in vivo images using methods in accordance with variousembodiments described herein, including multiple rings circular scanDoppler OCT in combination with SSADA to provide total retinal bloodflow (TRBF) measurement as mentioned above. The multiple ring circularscan was used to calculate the total retinal blood flow around the opticdisc using information from both arteries and veins. FIG. 17A shows anen face OCT angiogram computed with the SSADA technology disclosedherein. The vascular pattern, including branching and crossing, can beextracted using a graph search technique. FIG. 17B shows a cross-sectionSSADA angiogram providing precise positions of the top boundary ofretinal vessels (depicted with arrow heads.) FIG. 17C shows a colorDoppler display of a section of a circular scan showing the blue and redDoppler shifts in lumen cross-section that can be used for velocitycalculations. One vessel (as noted with a green arrowhead) is visible ondecorrelation-based angiography image shown in FIG. 17B, but is not onthe color Doppler image shown in FIG. 17C. This visualizes decorrelationangiography reliably defining vessel anatomy, while Doppler shift canprovide more quantitative velocity information. FIG. 17D shows acomposite image showing Doppler measurements (blue & red vesselsegments) from the circular scans overlaid on the en face summation viewof the 3D OCT scan. This visualization shows that the vessel trajectory(e.g, position, slope, and curvature fit by second order splinefunction,) branching, and crossing can be characterized by thecombination of multi-circular and 3D scans, thereby allowing foraccurate measurement of TRBF (e.g., by summing flow measurements inarteries and veins in a region and dividing by half.)

B. Additional Embodiments

Certain embodiments are directed to novel quantitative systems formeasuring ocular blood flow as described below. Specifically, thesesystems are able to measure circulation in ONH (e.g. whole disc/temporalellipse, peripapillary retina, peripapillary choroid) and macula (e.g.macular retina, macular choroid, fovea avascular zone and the area ofnon-perfusion). Quantification of different circulation obtained fromglaucoma patients compared with that from normal subjects is hereindescribed.

Acquisition of 3D Decorrelation Image Design of 3D Scan Pattern

The 3D scan pattern 100 to scan macula and disc of the eye is optimizedto implement split-spectrum amplitude decorrelation algorithm (SSADA)[Jia et al., Opt. Express 20:4710-4725 (2012)]. It is illustrated inFIG. 18. For each 3D volumetric data 102 comprising data obtained via anM-B-scan mode from an OCT system. M-B-scan 104 is composed by Nconsecutive B-scans at a single Y position (e.g. N=8). Therefore, foreach 3D volumetric data 102, in the fast scan axis, a single B-scancomprises a number of A-scans 106, and in the slow scan axis, there area number of M-B-scans 104 comprising N consecutive B-scans. For example,the scan pattern used for an OCT system with a scan speed of 100K axialscan/second can contain 200 A-lines covering 3 mm along fast scandirection, and contain 200 M-B-scans (N=8) along slow scan direction.One 3D data set 102 can be acquired in 3.4 seconds. The repeatedB-frames 104 are used for the SSADA calculation to obtain both thestructure and blood flow images.

Application of SSADA

As described previously [Jia et al., Opt. Express 20:4710-4725 (2012)],SSADA can split the spectrum into different frequency bands andcalculate decorrelation in each band separately (FIG. 19). Thedecorrelation (flow) images from the multiple spectral bands can then beaveraged together to make full use of the speckle information in theentire OCT spectrum. By splitting the full OCT spectral interferogramsinto several wavenumber bands, the OCT resolution cell in each band ismodified intentionally and less susceptible to axial motion noise.

For visualization purposes, the modified resolution cell is unnecessaryto be exactly isotropic; however, for quantification purposes, theisotropic resolution cell is the key point because it can be sensitiveto axial and transverse flow equally. In other words, decorrelationvalue derived from isotropic resolution cell is proportional to velocityregardless of direction. FIG. 19 is an example to explain how to splitraw interference spectrum 202 and create an isotropic resolution cell212. For a swept source OCT setup, the axial (Z) resolution in theresolution cell 206 is determined by the source central wavelength (1050nm) and its spectral bandwidth 204 (69 nm); and the lateral (X and Y)resolution is determined by the laser beam profile. Using thefull-width-half maximum (FWHM) amplitude profile definition, the axialresolution (5.3 μm) is 3.3 times higher than the lateral resolution(17.7 μm) in the resolution cell 206 where X=Y=3.3Z. In order to createan isotropic resolution cell 212 where X=Y=Z, spectrum should be splitinto different frequency bands with the new bandwidth 210 of 21 μm (e.g.split-spectrum 208). Please note the overlap between different spectralbands can be increased to further enhance image quality, which mightalso increase computing time.

Quantitative Blood Flow Assessment

A flowchart of quantitative blood flow measurements is provided in FIG.20. It begins with the detection of measurement flow volumes usingreliable anatomical landmarks on reflectance images and ends up with thecalculation of two blood flow parameters: flow index and vessel density.Additionally, the measurement of non-perfusion area is also demonstratedfor the quantification purpose.

Detection of Measurement Volumes

Based on the 3D volumetric data set 102 from the above scanning protocol100, SSADA algorithm 303 is performed on raw interference spectrum 302and both reflectance intensity images 304 and decorrelation (flow)images 306 can be obtained simultaneously. If necessary, the imagedistortion due to the saccadic motion artifacts can be reduced byperforming 3D registration 307 [Kraus et al., Biomed. Opt. Express3:1182-1199 (2012)]. When registered B-scan reflectance 308 andregistered B-scan flow 310 are processed, at 311, the anatomicallandmarks are identified on B-scan reflectance 308 and used for thesegmentation of B-scan flow 310. At 313, maximum projection algorithm[Jia et al., Opt. Express 20:4710-4725 (2012)] is applied on bothreflectance 308 and segmented flow 312 image. The projection algorithmfinds the maximum reflectance and decorrelation value for eachtransverse position, representing the highest reflectance and fastestflowing vessel lumen respectively. Next, at 317, the landmarks on enface reflectance 314 are identified and used to mask en face segmentedflow image 316. Then segmented en face flow image 318 is obtained andused for calculation of flow index 320 and vessel density 322.

FIG. 21 shows the specific segmentation within various ocular vascularbeds, which is also summarized in Table 2 below. At ONH region,segmentation at B-scan for whole disc 410 and temporal ellipse 420 issupplementary data and of lesser value, so all signals between internallimiting membrane (ILM) 411 and bottom can remain. The ellipse boundingneural canal opening (NCO) 412 can be selected for whole discangiography 413 and ellipse at temporal area (tilted along disc-foveaaxis) 422 for temporal ellipse angiography 423. For peripapillary retina430, the region between ILM 411 and the inner-segment/outer-segment(IS/OS) junction plane 431 is for retina and 0.5 mm wide regionsurrounding the disc ellipse 432 can be demarcated for peripapillaryregion. For peripapillary choroid 440, the region between retinalpigment epithelium (RPE) 441 and 50 μm below it is for choroid andperipaillary region is same as defined above. At macula region, macularetina 450 can be separated by segmenting the region between ILM 411 andIS/OS 431 at B-scan and 1 mm wide region 452 surrounding fovea avascularzone (FAZ) 451 at en face flow image to obtain perifovea macularangiography 453; macula choroid 460 can also be extracted by choosingthe region between RPE 441 and 50 μm below it at B-scan and a roundregion 462 centered at FAZ 451.

TABLE 2 Summary of segmentation of various vascular beds Segmentation atSegmentation at En Vascular beds B-scan face ONH - Whole Between ILM(411) Ellipse bounding NCO Disc (410) and bottom (412) ONH - TemporalBetween ILM (411) Ellipse at temporal Ellipse (420) and bottom area(tilted along disc- fovea axis) (422) Peripapillary Between ILM (411)0.5 mm wide region Retina (430) and IS/OS (431) (432) surrounding thedisc ellipse (412) Peripapillary Between RPE (441) 0.5 mm wide regionChoroid (440) and 50 μm below it (432) surrounding the disc ellipse(412) Macular Retina Between ILM (411) 1 mm wide region (452) (450) andIS/OS (431) surrounding FAZ (451) (D = 0.6 mm) Macular Choroid BetweenRPE (441) Round region (462) (460) and 50 μm below it (D = 2.6 mm)centered at FAZ (451)

Dividing boundaries at B-scans can be identified through the analysis ofthe reflectance gradient profiles in depth. The disc boundary can beidentified by detecting the boundary of NCO and the FAZ 451 boundary canbe identified by the retina thickness map on the macula on which thethinnest point is the center of FAZ 451 and the circle region withdiameter of 600 μm is the size of FAZ 451 [Roh and Weiter, “Retinal andChoroidal Circulation” in Ophthalmology, M. Yanoff and J. S. Duker eds.(Mo: Mosby Elsevier, St. Louis, 2008)]. Other boundary detection skillscan also be applied for the identification of landmarks.

It should be noted that in ONH scans, the IS/OS 431 and the RPE 441plane can be set by interpolating their depth just outside the discboundary for segmentation at B-scans. Other alternative segmentationmethods for ONH scans can also be used. For example, as shown by FIG.22, both retina 504 and ONH 506 can be segmented away from choroid 508through the entire 3D volume 502 and then segment the en face retina andONH angiography 510 and en face choroid angiography 512 to obtain wholedisc 410, temporal ellipse 420 and peripapillary retina 430 and choroid440. In addition, the choroid of 50 μm can be chosen to better representthe inner choroidal part, choriocapillaris layer [Roh and Weiter,“Retinal and Choroidal Circulation” in Ophthalmology, M. Yanoff and J.S. Duker eds. (Mo: Mosby Elsevier, St. Louis, 2008)] and to avoid theeffects from the signal voids in the outer choroid [Jia et al., Opt.Express 20:47190-4725 (2012)]. Alternatively, choroid thickness can alsobe slightly changed.

An example demonstrating how to detect optic disc boundary 412 andtemporal ellipse of disc 422 is described herein below and in FIG. 23.The NCO 604, which is the termination of the retinal pigment epithelium(RPE)/Bruch's membrane (BM) complex [Strouthidis et al., Invest.Ophthamol. Vis. Sci. 50:214-223 (2009) and Hu et al., Invest. Ophthamol.Vis. Sci. 51:5708-5717 (2010)], can be used to define the ONH margin 412[Strouthidis et al., Invest. Ophthamol. Vis. Sci. 50:4709-4718 (2009)].In OCT cross-sectional B-scans, the boundary of the NCO 604, indicatedby two green dashed lines 606 is determined by the termination of theRPE/BM complex (two green arrows) 604. It corresponds to the two marginpoints 608 at the disc region. These representative margin points aretransferred on the corresponding flow cross-sectional image 610 and enface projection maps 612. The boundary produced by the surroundingperipapillary choroidal blood flow 614 in the flow data 502 is notoptimally used as the detection method for disc margin 412.

Because the NCO 604 is detectable, it can be manually delineated on thereflectance projection image 314. Shown by FIG. 24, the major 702 andminor axes 704 of the ellipse are calculated along the vertical andhorizontal directions. The approximate center of disc is the centroid ofthe selected ellipse 706.

After the position and dimensions of a disc are determined, the wholedisc 412 and temporal ellipse areas 422 can be segmented forquantitative analysis. The optic disc mask 708 is idealized as anellipse with vertical diameter (VD) 702 and horizontal diameter (HD)704. The mask value is 1 inside the ellipse and 0 outside. An ellipticalmask 710 is also defined for the disc region temporal to the majorsuperior and inferior branch arteries and veins. The temporal ellipse422 has a major axis diameter of 0.75 VD and a minor axis diameter of0.50 HD. The temporal ellipse is tilted inferiorly to fit the tilt ofthe disc vessel pattern associated with the tilt of the disc-fovea axis712. According to the literature measurements on the normal population[J. M. McDonnel, “Ocular embryology and anatomy,” in Retina, S. J. Ryan,ed. (CV Mosby, St Louis, 1989), pp. 5-16], the average value of thisangle is 7.1°. The temporal ellipse angiography 423 does not contain anymajor branch retinal blood vessels and therefore may be a better measureof local disc microcirculation.

Definition and Calculation of Flow Index and Vessel Density

By multiplying different masks 317 with original blood flow projectionmap 316, respectively, the segmented flow maps 318 can be acquired forfurther quantification.

The flow index 320 is defined as the average decorrelation values in thesegmented area, which can be given by,

$\begin{matrix}\begin{matrix}\frac{\int_{A}{{D \cdot V}{A}}}{\int_{A}{A}} & \left( {{V = 1},{{{if}\mspace{14mu} {vessel}};{V = 0}},{{if}\mspace{14mu} {not}}} \right)\end{matrix} & (1)\end{matrix}$

The vessel density is defined as the percentage area occupied by vesselsin the segmented area, using the following formula,

$\begin{matrix}\begin{matrix}\frac{\int_{A}{V{A}}}{\int_{A}{A}} & \left( {{V = 1},{{{if}\mspace{14mu} {vessel}};{V = 0}},{{if}\mspace{14mu} {not}}} \right)\end{matrix} & (2)\end{matrix}$

Where A can be segmented en face flow area 318, D is the decorrelationvalue acquired by SSADA 303. The threshold used to judge V is 1 or 0 andwas set at 0.125, two standard deviations above the mean decorrelationvalue in noise region. As described by previous report [Jia et al., Opt.Express 20:4710-4725 (2012)], the central FAZ 451 in the normal eyes canbe chosen as a noise region after the same scanning pattern 100 wasapplied on the macular region, and then calculated decorrelation valuesby SSADA 303.

Detection Fovea Avascular Zone and Other Areas of Non-Perfusion

First, a macular retinal angiography 802 of 200×200 pixels, as shown inFIG. 25, is obtained by projecting 3D retinal vasculature 502 segmentedbetween ILM 411 and IS/OS 431. Then this angiography 802 can be smoothedthrough a Gaussian filter 803. A Gaussian filter 803 is an effectivesmoothing filter for random noise reduction. In practice, the standarddeviation and the window size of a Gaussian filter 803 should be chosenwith care to smooth the capillary bed around FAZ 451. Their values areusually chosen based on the diameters of main retinal vessel branches804. Typically, a σ value of 75 μm is chosen with a window size of 150μm×150 μm because visually they generate smoothed 2D angiogram 806 forthe subsequent analyses. The non-perfusion information 808 can becomputed by identifying the pixels whose values are lower than the noisefloor 807. In this part, the parameters chosen for Gaussian filter 803can be varied according to the pixel size.

C. Further Embodiments

Preliminary studies were carried out utilizing the technology describedherein. The swept-source OCT system was operated at the centerwavelength of 1050 nm, speed of 100,000 axial scans per second, axialresolution of 5 μm and spot diameter of 18 μm (FWHM amplitude). Withthis configuration and the scan pattern described previously, the B-scanframe rate of the system was 500 frames per second; therefore, 1600B-scans were acquired to form a 3D data cube, corresponding to anacquisition time of 3.4 seconds.

Both normal and glaucoma groups were studied. Within glaucoma group, twoperimetric glaucoma subjects, three pre-perimetric glaucoma subjects andone suspect subjects with ocular hypertension were enrolled for study.In normal subjects, a dense microvascular network was visible on the OCTangiography of the disc 902, in addition to large retinal vessels. Thisnetwork was visibly attenuated in all PPG subjects, as shown in the disc904 of FIG. 26. This difference cannot be visualized and quantified bydisc photographs normal 906 & PPG 908 respectively. Within the wholedisc region, the flow index was reduced by 32% in the glaucoma group.These reductions were statistically significant (see Table 3 below).Regional disc perfusion measurements on the temporal ellipse showeddifferences between two groups were statistically significant. Thus theestimated reductions were higher than those based on the whole discregion. For this small region quantification, the repeatabilities of themeasurements of flow index at whole disc and temporal ellipse were 6.6%and 7.9% respectively. Quantification on peripapillary retina,peripapillary choroid and central macula didn't show any statisticaldifference between two groups (Table 3).

TABLE 3 Quantification of different vascular beds in glaucoma Flow indexGlaucoma + P-value (dimensionless) Normal Suspect (Wilcoxon) ONH - Whole0.159 ± 0.020 0.108 ± 0.013 0.008 Disc ONH - Temporal 0.151 ± 0.0140.072 ± 0.022 0.005 Ellipse Peripapillary 0.141 ± 0.023 0.122 ± 0.0240.191 Retina Peripapillary 0.226 ± 0.016 0.168 ± 0.071 0.105 ChoroidMacular Retina 0.120 ± 0.017 0.112 ± 0.039 0.819

In the techniques described herein, the major superior and inferiorbranches of the retinal vessels on the temporal side were excluded, andquantifications focused mainly on ONH microvascular beds. Preliminaryresults suggest that in early glaucoma the reduction of ONHmicrovascular flow is much more dramatic than that of whole ONHcirculation. This suggests that quantification performed onmicrovascular perfusion may be more sensitive for detecting ONHcirculatory changes in early glaucoma patients.

For ONH angiography, ONH flow index and peripapillary choroidal flowindex are important for the diagnosis and evaluation of glaucoma.Differences between normal and glaucoma in a pilot clinical study wereshown. Macular angiography is useful for macular diseases. Perifovealretinal flow index and macular retinal flow map (size of fovealavascular zone, and identification of any other nonperfusion area) areimportant for the evaluation of macular ischemia in diabeticretinopathy. Macular choroidal flow index and macular choroidal flow mapare important in the evaluation of AMD.

In one embodiment, for the diagnosis of age-related maculardegeneration, measuring the flow index of and detecting an impairment inblood flow in either/both of the choroidal neovascular membrane and/ormacular choroid is of primary importance. In another embodiment, for thediagnosis of glaucoma, measuring the flow index of and detecting animpairment in blood flow in the ocular nerve head is of primaryimportance. Finally, in yet an additional embodiment, for the diagnosisof diabetic retinopathy, measuring the flow index of and detecting animpairment in blood flow in the perifoveal avascular region is ofprimary importance. Data confirming the above described correlations notshown (manuscripts in preparation).

The disclosure set forth above encompasses multiple distinctembodiments. While each of these embodiments have been disclosed in itspreferred form, the specific embodiments as disclosed and illustratedherein are not to be considered in a limiting sense as numerousvariations are possible. The subject matter of the present disclosureincludes all novel and non-obvious combinations and subcombinations ofthe various elements, features, functions and/or properties disclosedherein. Similarly, where any claim recites “a” or “a first” element orthe equivalent thereof, such claim should be understood to includeincorporation of one or more such elements, neither requiring norexcluding two or more such elements.

What is claimed is:
 1. A method for quantitatively measuring blood flowin an ocular vascular bed, said method comprising: selecting an ocularvascular bed from which to quantitatively measure blood flow; scanningsaid ocular vascular bed to obtain M-B scans of OCT spectrum therefrom;splitting the M-B scans of OCT spectrum into M spectral bands; anddetermining a quantitative measurement of blood flow from the M spectralbands.
 2. The method of claim 1, wherein the step of splitting the M-Bscans of the OCT spectrum into M spectral bands comprises: creatingoverlapping filters covering the OCT spectrum; and filtering the OCTspectrum with the overlapping filters.
 3. The method of claim 1, whereinthe step of determining a quantitative measurement of blood flow fromthe M spectral bands comprises: creating decorrelation images for the Mspectral bands; and combining the decorrelation images for the Mspectral bands to create a flow image.
 4. The method of claim 3, whereinthe step of creating decorrelation images for the M spectral bandscomprises: determining amplitude information for each spectral band; andcalculating decorrelation between adjacent amplitude frames for eachspectral band.
 5. The method of claim 4 further comprising the step ofremoving background noise.
 6. The method of claim 3, wherein the step ofcombining the decorrelation images for the M spectral bands to create aflow image comprises: averaging the decorrelation images for eachspectral band to create an average decorrelation image for each spectralband; and averaging the averaged decorrelation images from the Mspectral bands.
 7. The method of claim 6, further comprising the step ofeliminating decorrelation images for each spectral band having excessivemotion noise.
 8. The method of claim 1, wherein the ocular vascular bedis selected from the group consisting of the ocular nerve head and themacula.
 9. The method of claim 1, wherein the ocular vascular bed isselected from the group consisting of the ocular disc, the temporalellipse, the peripapillary retina, the peripapillary choroid, themacular retina, the macular choroid, the fovea avascular zone, and thearea of non-perfusion.
 10. A method of diagnosing the presence of anocular disease in a subject suspected of having said ocular disease,said method comprising: selecting a specific ocular vascular bed in saidsubject from which to quantitatively measure blood flow; scanning saidocular vascular bed to obtain M-B scans of OCT spectrum therefrom;splitting the M-B scans of OCT spectrum into M spectral bands;determining a quantitative measurement of blood flow from the M spectralbands; and comparing said quantitative measurement of blood flow to thatobtained from a normal subject, wherein a decrease in blood flow in thepatient suspected of having said ocular disease as compared to thatobtained from said normal subject is indicative of the presence of saiddisease.
 11. The method of claim 10, wherein said ocular disease isglaucoma.
 12. The method of claim 11, wherein the specific ocularvascular bed is the optic nerve head.
 13. The method of claim 10,wherein said ocular disease is diabetic retinopathy.
 14. The method ofclaim 13, wherein the specific ocular vascular bed is the perifovealavascular zone
 15. The method of claim 10, wherein said ocular diseaseis age-related macular degeneration.
 16. The methods of claim 15,wherein the specific ocular vascular bed is the choroidal neovascularmembrane or the macular choroid.
 17. The method of claim 10, wherein theocular vascular bed is selected from the group consisting of the ocularnerve head and the macula.
 18. The method of claim 10, wherein theocular vascular bed is selected from the group consisting of the oculardisc, the temporal ellipse, the peripapillary retina, the peripapillarychoroid, the macular retina, the macular choroid, the fovea avascularzone, and the area of non-perfusion.
 19. A system for quantitativelymeasuring blood flow in an ocular vascular bed, said system comprising:an optical coherence tomography apparatus; and one or more processorscoupled to said apparatus and adapted to cause said apparatus to obtainM-B scans of OCT spectrum from said ocular vascular bed, split the M-Bscans of OCT spectrum into M spectral bands, and determine aquantitative measurement of blood flow from the M spectral bands. 20.The system of claim 19, wherein the one or more processors adapted tocause the apparatus to split the M-B scans of OCT spectrum into Mspectral bands further comprises being adapted to cause the apparatus tocreate overlapping filters covering the OCT spectrum and filter said OCTspectrum with the overlapping filters.
 21. The system of claim 19,wherein the one or more processors adapted to cause the apparatus todetermine a quantitative measurement of blood flow from the M spectralbands further comprises being adapted to cause the apparatus to createdecorrelation images for the M spectral bands and combine thedecorrelation images for the M spectral bands to determine aquantitative measurement of blood flow.
 22. The system of claim 21,wherein the one or more processors adapted to cause the apparatus tocreate decorrelation images for M spectral bands further comprises beingadapted to cause the apparatus to determine amplitude information foreach spectral band and calculate decorrelation between adjacentamplitude frames for each spectral band.
 23. The system of claim 21,wherein the one or more processors adapted to cause the apparatus tocombine the decorrelation images for the M spectral bands to determine aquantitative measurement of blood flow further comprises being adaptedto cause the apparatus to average the decorrelation images for eachspectral band to create and average decorrelation image for eachspectral band and average the averaged decorrelation images from the Mspectral bands.